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Video Quality Assessment and Comparative Evaluation of Peer-to-Peer Video Streaming Systems Sachin Agarwal Jatinder Pal Singh Deutsche Telekom A.G., Laboratories Berlin, Germany Aditya Mavlankar Pierpaolo Baccichet Bernd Girod Stanford University Stanford CA, USA Outline

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video quality assessment and comparative evaluation of peer to peer video streaming systems

Video Quality Assessment and Comparative Evaluation of Peer-to-Peer Video Streaming Systems

Sachin Agarwal

Jatinder Pal Singh

Deutsche Telekom A.G., Laboratories

Berlin, Germany

Aditya Mavlankar Pierpaolo Baccichet Bernd Girod

Stanford University

Stanford CA, USA

outline
Outline
  • Introduction to P2P live video streaming
  • Prior work on system performance assessment
  • Test-bed setup
  • Performance of tested systems
p2p live video streaming
P2P Live Video Streaming
  • Extension of P2P file-sharing
  • Low-cost and scalable delivery mechanism
  • Several deployed commercial implementations today
  • Increasing content / channels available
related work on performance assessment
Related Work on Performance Assessment
  • Networking related metrics, e.g. bandwidth usage, packet loss, continuity index, etc.
    • CoolStreaming [Zhang et al., 2005]: PlanetLab
    • PPLive [Hei et al., 2006]: packet sniffing and crawling
    • SopCast [Sentinelli et al., 2007]: “watching”, PlanetLab
    • . . .
  • No video PSNR results
  • No repeatable test conditions
    • Network conditions
    • Encoded video characteristics
    • Peer behavior
  • No fair head-to-head comparison of different systems
test bed setup
Test-Bed Setup

576 X 9

1024 X 5

2048 X 1

Test center

Berlin, Germany

Server 1, 2

Berlin, Germany (15)

[Emulated HS Broadband]

PLR, delay, jitter and bandwidth measured for representative real connections and emulated using NISTNet traffic shaper

52 Mbps

576 X 16

1024 X 5

2048 X 1

Internet

Stanford, CA (22)

[Emulated HS Broadband]

ISP

Datacenter

Erfurt, Germany

128 X 2

192 X 2

576 X 2

1024 X 2

Berlin, Germany (8)

[Real HS Broadband]

TU Munich, Germany (3)

[Emulated HS Broadband]

3072 X 3

encoded video stream
Encoded Video Stream
  • La Dolce Vita (Fellini, 1960)
  • 24 fps, 352x240 pixels
  • H.264/AVC video codec, 400 kbit/sec CBR bitstream, 42 dB PSNR
  • I B B P B B P B B P . . . (I frame every second)
  • H.264 bitstream wrapped in Microsoft ASF container, if required by tested system
  • Last frame error concealment
peer churn model
Peer Churn Model
  • 30-minute simulation run
  • During each 6-minute time-slot
    • Peer on with probability 0.9
    • Peer off with probability 0.1
    • Peer can switch off for the rest of the run with probability 0.05
  • During last 5 minutes, peer off with probability 0.5
representative results
Representative Results
  • Tested systems
    • System A: Tree-based, push approach
    • System B: Mesh-based, data-driven or pull approach
  • Emulation runs
    • Run 1: with traffic shaping (using NISTNet)
    • Run 2: without traffic shaping
  • Same realization of peer On-Off model for all runs
pre roll delay
Pre-Roll Delay

about 30 sec enough for System A (tree-based)

about 60 sec enough for System B (mesh-based)

psnr drop w traffic shaping
PSNR Drop (w/ traffic shaping)

System A (tree-based)

System B (mesh-based)

32

psnr drop w o traffic shaping
PSNR Drop (w/o traffic shaping)

System A (tree-based)

System B (mesh-based)

32

statistics of frame freezes
Statistics of Frame Freezes
  • Frames frozen (as percentage of total frames to be displayed)
  • Average no. of distinct frame-freeze events per client in 30 min.
statistics of frame freezes cont
Statistics of Frame Freezes (cont.)

System A (tree-based) employs content-aware prioritization

Long frame freezes more likely with System B (mesh-based)

no of peers failing to decode a frame
No. of Peers Failing to Decode a Frame

System A (tree-based), Run 1

System B (mesh-based), Run 1

redundancy server load and parent peer analysis
Redundancy, Server Load and Parent-Peer Analysis
  • Redundancy (bytes received in excess of required video stream bytes)
    • System A (tree-based): 6% in both runs
    • System B (mesh-based): 35% and 20% in Runs 1 (w/ traffic shaping) and 2 (w/o traffic shaping) respectively
  • For both Systems, peer receives on average less than 10% of its data directly from the server; slightly more for Run 2 of System B
  • System A (tree-based): Sustained downloads from lower number of parent peers
summary
Summary
  • Proposed methodology allows measuring video PSNR, buffering time, frame-freeze statistics, peers failing to decode a frame, etc. beyond network usage, packet loss, etc.
  • Test conditions chosen by analyzing real-world conditions and experiments are repeatable
  • Tested three commercial-grade P2P video streaming systems
  • Room for improvement in current systems:
    • Long buffering time (10s of seconds)
    • Display freezes for more than 100 frames
  • Tested tree-based system outperforms mesh-based system:
    • Redundancy
    • Buffering time
thank you http www stanford edu maditya publication html related agarwal et al tridentcom 2008

Thank you!http://www.stanford.edu/~maditya/publication.htmlRelated:[Agarwal, et al., TRIDENTCOM 2008]